






"""simple docstring"""


import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple

from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
		__SCREAMING_SNAKE_CASE   =   'pt'
elif is_tf_available():
		__SCREAMING_SNAKE_CASE   =   'tf'
else:
		__SCREAMING_SNAKE_CASE   =   'jax'




class 						__UpperCamelCase     (     UpperCamelCase   ,     unittest.TestCase						):
					lowercase_    :				int      =  PerceiverTokenizer
					lowercase_    :				Any      =  False
					def        UpperCAmelCase__ (							self							:	Tuple   )	->       Tuple:
											super().setUp()
											lowerCAmelCase   :Any						=			PerceiverTokenizer()
											tokenizer.save_pretrained(self.tmpdirname   )
					@cached_property
					def        UpperCAmelCase__ (							self							:	List[str]   )	->       Any:
											return PerceiverTokenizer.from_pretrained('deepmind/language-perceiver'   )
					def        UpperCAmelCase__ (							self							:	Optional[int]     , **UpperCAmelCase							:	Any   )	->       PerceiverTokenizer:
											return self.tokenizer_class.from_pretrained(self.tmpdirname     , **UpperCAmelCase   )
					def        UpperCAmelCase__ (							self							:	int     , UpperCAmelCase							:	Any     , UpperCAmelCase							:	Union[str, Any]=False     , UpperCAmelCase							:	List[Any]=20     , UpperCAmelCase							:	Union[str, Any]=5   )	->       Tuple[str, list]:
											# XXX The default common tokenizer tests assume that every ID is decodable on its own.
											# This assumption is invalid for Perceiver because single bytes might not be
											# valid utf-8 (byte 128 for instance).
											# Here we're overriding the smallest possible method to provide
											# a clean sequence without making the same assumption.

											lowerCAmelCase   :int						=			[]
											for i in range(len(UpperCAmelCase   )   ):
																	try:
																							lowerCAmelCase   :int						=			tokenizer.decode([i]     , clean_up_tokenization_spaces=UpperCAmelCase   )
																	except UnicodeDecodeError:
																							pass
																	toks.append((i, tok)   )

											lowerCAmelCase   :Optional[int]						=			list(filter(lambda UpperCAmelCase   : re.match(r'^[ a-zA-Z]+$'     , t[1]   )     , UpperCAmelCase   )   )
											lowerCAmelCase   :List[str]						=			list(filter(lambda UpperCAmelCase   : [t[0]] == tokenizer.encode(t[1]     , add_special_tokens=UpperCAmelCase   )     , UpperCAmelCase   )   )
											if max_length is not None and len(UpperCAmelCase   ) > max_length:
																	lowerCAmelCase   :str						=			toks[:max_length]
											if min_length is not None and len(UpperCAmelCase   ) < min_length and len(UpperCAmelCase   ) > 0:
																	while len(UpperCAmelCase   ) < min_length:
																							lowerCAmelCase   :Any						=			toks + toks
        # toks_str = [t[1] for t in toks]
											lowerCAmelCase   :Any						=			[t[0] for t in toks]

											# Ensure consistency
											lowerCAmelCase   :List[str]						=			tokenizer.decode(UpperCAmelCase     , clean_up_tokenization_spaces=UpperCAmelCase   )
											if " " not in output_txt and len(UpperCAmelCase   ) > 1:
																	lowerCAmelCase   :List[str]						=			(
																	    tokenizer.decode([toks_ids[0]]     , clean_up_tokenization_spaces=UpperCAmelCase   )
																	    + ' '
																	    + tokenizer.decode(toks_ids[1:]     , clean_up_tokenization_spaces=UpperCAmelCase   )
																	)
											if with_prefix_space:
																	lowerCAmelCase   :Any						=			' ' + output_txt
											lowerCAmelCase   :List[Any]						=			tokenizer.encode(UpperCAmelCase     , add_special_tokens=UpperCAmelCase   )
											return output_txt, output_ids
					def        UpperCAmelCase__ (							self							:	Optional[Any]   )	->       Tuple:
											lowerCAmelCase   :Optional[int]						=			self.perceiver_tokenizer
											lowerCAmelCase   :str						=			'Unicode €.'
											lowerCAmelCase   :int						=			tokenizer(UpperCAmelCase   )
											lowerCAmelCase   :List[str]						=			[4, 91, 116, 111, 105, 117, 106, 107, 38, 232, 136, 178, 52, 5]
											self.assertEqual(encoded['input_ids']     , UpperCAmelCase   )

											# decoding
											lowerCAmelCase   :List[Any]						=			tokenizer.decode(UpperCAmelCase   )
											self.assertEqual(UpperCAmelCase     , '[CLS]Unicode €.[SEP]'   )

											lowerCAmelCase   :str						=			tokenizer('e è é ê ë'   )
											lowerCAmelCase   :Tuple						=			[4, 107, 38, 201, 174, 38, 201, 175, 38, 201, 176, 38, 201, 177, 5]
											self.assertEqual(encoded['input_ids']     , UpperCAmelCase   )
											# decoding
											lowerCAmelCase   :str						=			tokenizer.decode(UpperCAmelCase   )
											self.assertEqual(UpperCAmelCase     , '[CLS]e è é ê ë[SEP]'   )

											# encode/decode, but with `encode` instead of `__call__`
											self.assertEqual(tokenizer.decode(tokenizer.encode('e è é ê ë'   )   )     , '[CLS]e è é ê ë[SEP]'   )
					def        UpperCAmelCase__ (							self							:	Optional[int]   )	->       str:
											lowerCAmelCase   :Union[str, Any]						=			self.perceiver_tokenizer
											lowerCAmelCase   :List[Any]						=			['A long paragraph for summarization.', 'Another paragraph for summarization.']
											# fmt: off
											lowerCAmelCase   :List[str]						=			[4, 71, 38, 114, 117, 116, 109, 38, 118, 103, 120, 103, 109, 120, 103, 118, 110, 38, 108, 117, 120, 38, 121, 123, 115, 115, 103, 120, 111, 128, 103, 122, 111, 117, 116, 52, 5, 0]
											# fmt: on
											lowerCAmelCase   :Any						=			tokenizer(UpperCAmelCase     , padding=UpperCAmelCase     , return_tensors=UpperCAmelCase   )
											self.assertIsInstance(UpperCAmelCase     , UpperCAmelCase   )

											if FRAMEWORK != "jax":
																	lowerCAmelCase   :Any						=			list(batch.input_ids.numpy()[0]   )
											else:
																	lowerCAmelCase   :List[Any]						=			list(batch.input_ids.tolist()[0]   )

											self.assertListEqual(UpperCAmelCase     , UpperCAmelCase   )

											self.assertEqual((2, 38)     , batch.input_ids.shape   )
											self.assertEqual((2, 38)     , batch.attention_mask.shape   )
					def        UpperCAmelCase__ (							self							:	List[str]   )	->       Tuple:
											lowerCAmelCase   :Dict						=			self.perceiver_tokenizer
											lowerCAmelCase   :List[Any]						=			['A long paragraph for summarization.', 'Another paragraph for summarization.']
											lowerCAmelCase   :str						=			tokenizer(UpperCAmelCase     , padding=UpperCAmelCase     , return_tensors=UpperCAmelCase   )
											# check if input_ids are returned and no decoder_input_ids
											self.assertIn('input_ids'     , UpperCAmelCase   )
											self.assertIn('attention_mask'     , UpperCAmelCase   )
											self.assertNotIn('decoder_input_ids'     , UpperCAmelCase   )
											self.assertNotIn('decoder_attention_mask'     , UpperCAmelCase   )
					def        UpperCAmelCase__ (							self							:	List[Any]   )	->       str:
											lowerCAmelCase   :List[str]						=			self.perceiver_tokenizer
											lowerCAmelCase   :List[str]						=			[
											    'Summary of the text.',
											    'Another summary.',
											]
											lowerCAmelCase   :Optional[Any]						=			tokenizer(
											    text_target=UpperCAmelCase     , max_length=32     , padding='max_length'     , truncation=UpperCAmelCase     , return_tensors=UpperCAmelCase   )
											self.assertEqual(32     , targets['input_ids'].shape[1]   )
					def        UpperCAmelCase__ (							self							:	List[str]   )	->       Union[str, Any]:
											# safety check on max_len default value so we are sure the test works
											lowerCAmelCase   :List[Any]						=			self.get_tokenizers()
											for tokenizer in tokenizers:
																	with self.subTest(f"""{tokenizer.__class__.__name__}"""   ):
																							self.assertNotEqual(tokenizer.model_max_length     , 42   )

        # Now let's start the test
											lowerCAmelCase   :List[str]						=			self.get_tokenizers()
											for tokenizer in tokenizers:
																	with self.subTest(f"""{tokenizer.__class__.__name__}"""   ):
																							# Isolate this from the other tests because we save additional tokens/etc
																							lowerCAmelCase   :List[str]						=			tempfile.mkdtemp()

																							lowerCAmelCase   :Union[str, Any]						=			' He is very happy, UNwant\u00E9d,running'
																							lowerCAmelCase   :Optional[int]						=			tokenizer.encode(UpperCAmelCase     , add_special_tokens=UpperCAmelCase   )
																							tokenizer.save_pretrained(UpperCAmelCase   )

																							lowerCAmelCase   :List[Any]						=			tokenizer.__class__.from_pretrained(UpperCAmelCase   )
																							lowerCAmelCase   :Tuple						=			after_tokenizer.encode(UpperCAmelCase     , add_special_tokens=UpperCAmelCase   )
																							self.assertListEqual(UpperCAmelCase     , UpperCAmelCase   )

																							shutil.rmtree(UpperCAmelCase   )

											lowerCAmelCase   :int						=			self.get_tokenizers(model_max_length=42   )
											for tokenizer in tokenizers:
																	with self.subTest(f"""{tokenizer.__class__.__name__}"""   ):
																							# Isolate this from the other tests because we save additional tokens/etc
																							lowerCAmelCase   :Any						=			tempfile.mkdtemp()

																							lowerCAmelCase   :Optional[int]						=			' He is very happy, UNwant\u00E9d,running'
																							tokenizer.add_tokens(['bim', 'bambam']   )
																							lowerCAmelCase   :List[Any]						=			tokenizer.additional_special_tokens
																							additional_special_tokens.append('new_additional_special_token'   )
																							tokenizer.add_special_tokens({'additional_special_tokens': additional_special_tokens}   )
																							lowerCAmelCase   :Dict						=			tokenizer.encode(UpperCAmelCase     , add_special_tokens=UpperCAmelCase   )
																							tokenizer.save_pretrained(UpperCAmelCase   )

																							lowerCAmelCase   :List[str]						=			tokenizer.__class__.from_pretrained(UpperCAmelCase   )
																							lowerCAmelCase   :Dict						=			after_tokenizer.encode(UpperCAmelCase     , add_special_tokens=UpperCAmelCase   )
																							self.assertListEqual(UpperCAmelCase     , UpperCAmelCase   )
																							self.assertIn('new_additional_special_token'     , after_tokenizer.additional_special_tokens   )
																							self.assertEqual(after_tokenizer.model_max_length     , 42   )

																							lowerCAmelCase   :Tuple						=			tokenizer.__class__.from_pretrained(UpperCAmelCase     , model_max_length=43   )
																							self.assertEqual(tokenizer.model_max_length     , 43   )

																							shutil.rmtree(UpperCAmelCase   )
					def        UpperCAmelCase__ (							self							:	str   )	->       Dict:
											lowerCAmelCase   :Dict						=			[]
											if self.test_slow_tokenizer:
																	tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())   )

											if self.test_rust_tokenizer:
																	tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())   )

											for tokenizer_class, tokenizer_utils in tokenizer_list:
																	with tempfile.TemporaryDirectory() as tmp_dir:
																							tokenizer_utils.save_pretrained(UpperCAmelCase   )

																							with open(os.path.join(UpperCAmelCase     , 'special_tokens_map.json'   )     , encoding='utf-8'   ) as json_file:
																													lowerCAmelCase   :Optional[Any]						=			json.load(UpperCAmelCase   )

																							with open(os.path.join(UpperCAmelCase     , 'tokenizer_config.json'   )     , encoding='utf-8'   ) as json_file:
																													lowerCAmelCase   :Optional[Any]						=			json.load(UpperCAmelCase   )

																							lowerCAmelCase   :List[str]						=			[f"""<extra_id_{i}>""" for i in range(125   )]

																							lowerCAmelCase   :Optional[int]						=			added_tokens_extra_ids + [
																							    'an_additional_special_token'
																							]
																							lowerCAmelCase   :int						=			added_tokens_extra_ids + [
																							    'an_additional_special_token'
																							]

																							with open(os.path.join(UpperCAmelCase     , 'special_tokens_map.json'   )     , 'w'     , encoding='utf-8'   ) as outfile:
																													json.dump(UpperCAmelCase     , UpperCAmelCase   )
																							with open(os.path.join(UpperCAmelCase     , 'tokenizer_config.json'   )     , 'w'     , encoding='utf-8'   ) as outfile:
																													json.dump(UpperCAmelCase     , UpperCAmelCase   )

																							# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
																							# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
																							# "special_tokens_map.json" files
																							lowerCAmelCase   :str						=			tokenizer_class.from_pretrained(
																							    UpperCAmelCase     , )
																							self.assertIn(
																							    'an_additional_special_token'     , tokenizer_without_change_in_init.additional_special_tokens   )
																							self.assertEqual(
																							    ['an_additional_special_token']     , tokenizer_without_change_in_init.convert_ids_to_tokens(
																							        tokenizer_without_change_in_init.convert_tokens_to_ids(['an_additional_special_token']   )   )     , )

																							# Now we test that we can change the value of additional_special_tokens in the from_pretrained
																							lowerCAmelCase   :List[Any]						=			added_tokens_extra_ids + [AddedToken('a_new_additional_special_token'     , lstrip=UpperCAmelCase   )]
																							lowerCAmelCase   :str						=			tokenizer_class.from_pretrained(
																							    UpperCAmelCase     , additional_special_tokens=UpperCAmelCase     , )

																							self.assertIn('a_new_additional_special_token'     , tokenizer.additional_special_tokens   )
																							self.assertEqual(
																							    ['a_new_additional_special_token']     , tokenizer.convert_ids_to_tokens(
																							        tokenizer.convert_tokens_to_ids(['a_new_additional_special_token']   )   )     , )
					def        UpperCAmelCase__ (							self							:	str   )	->       int:
											lowerCAmelCase   :Dict						=			self.perceiver_tokenizer
											self.assertEqual(tokenizer.decode([178]   )     , '�'   )
					def        UpperCAmelCase__ (							self							:	List[Any]   )	->       Dict:
											pass
					def        UpperCAmelCase__ (							self							:	Optional[int]   )	->       List[Any]:
											pass
					def        UpperCAmelCase__ (							self							:	List[str]   )	->       Union[str, Any]:
											pass
					def        UpperCAmelCase__ (							self							:	List[str]   )	->       Optional[Any]:
											pass
					def        UpperCAmelCase__ (							self							:	Union[str, Any]   )	->       Any:
											# The default common tokenizer tests uses invalid tokens for Perceiver that can only accept one-character
											# strings and special added tokens as tokens
											lowerCAmelCase   :Any						=			self.get_tokenizers(fast=UpperCAmelCase     , do_lower_case=UpperCAmelCase   )
											for tokenizer in tokenizers:
																	with self.subTest(f"""{tokenizer.__class__.__name__}"""   ):
																							lowerCAmelCase   :Tuple						=			['[CLS]', 't', 'h', 'i', 's', ' ', 'i', 's', ' ', 'a', ' ', 't', 'e', 's', 't', '[SEP]']
																							lowerCAmelCase   :Dict						=			tokenizer.convert_tokens_to_string(UpperCAmelCase   )

																							self.assertIsInstance(UpperCAmelCase     , UpperCAmelCase   )







"""simple docstring"""


from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
		from PIL import Image

		from ..image_utils import load_image

if is_tf_available():
		from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_VISION_2_SEQ_MAPPING

if is_torch_available():
		import torch

		from ..models.auto.modeling_auto import MODEL_FOR_VISION_2_SEQ_MAPPING

__SCREAMING_SNAKE_CASE   =   logging.get_logger(__name__)




@add_end_docstrings(UpperCamelCase						)
class 						__UpperCamelCase     (     UpperCamelCase						):
					def __init__(							self							:	List[Any]     , *UpperCAmelCase							:	Any     , **UpperCAmelCase							:	Tuple   )	->       Optional[int]:
											super().__init__(*UpperCAmelCase     , **UpperCAmelCase   )
											requires_backends(self     , 'vision'   )
											self.check_model_type(
											    TF_MODEL_FOR_VISION_2_SEQ_MAPPING if self.framework == 'tf' else MODEL_FOR_VISION_2_SEQ_MAPPING   )
					def        UpperCAmelCase__ (							self							:	List[str]     , UpperCAmelCase							:	str=None     , UpperCAmelCase							:	str=None     , UpperCAmelCase							:	List[str]=None   )	->       Tuple:
											lowerCAmelCase   :Optional[Any]						=			{}
											lowerCAmelCase   :int						=			{}

											if prompt is not None:
																	lowerCAmelCase   :Any						=			prompt

											if generate_kwargs is not None:
																	lowerCAmelCase   :List[Any]						=			generate_kwargs
											if max_new_tokens is not None:
																	if "generate_kwargs" not in forward_kwargs:
																							lowerCAmelCase   :Any						=			{}
																	if "max_new_tokens" in forward_kwargs["generate_kwargs"]:
																							raise ValueError(
																							    '\'max_new_tokens\' is defined twice, once in \'generate_kwargs\' and once as a direct parameter,'
																							    ' please use only one'   )
																	lowerCAmelCase   :Union[str, Any]						=			max_new_tokens
											return preprocess_params, forward_kwargs, {}
					def __call__(							self							:	Tuple     , UpperCAmelCase							:	Union[str, List[str], "Image.Image", List["Image.Image"]]     , **UpperCAmelCase							:	Any   )	->       Optional[int]:
											return super().__call__(UpperCAmelCase     , **UpperCAmelCase   )
					def        UpperCAmelCase__ (							self							:	List[str]     , UpperCAmelCase							:	Tuple     , UpperCAmelCase							:	Any=None   )	->       str:
											lowerCAmelCase   :Dict						=			load_image(UpperCAmelCase   )

											if prompt is not None:
																	if not isinstance(UpperCAmelCase     , UpperCAmelCase   ):
																							raise ValueError(
																							    f"""Received an invalid text input, got - {type(UpperCAmelCase   )} - but expected a single string. """
																							    'Note also that one single text can be provided for conditional image to text generation.'   )

																	lowerCAmelCase   :List[str]						=			self.model.config.model_type

																	if model_type == "git":
																							lowerCAmelCase   :Any						=			self.image_processor(images=UpperCAmelCase     , return_tensors=self.framework   )
																							lowerCAmelCase   :Union[str, Any]						=			self.tokenizer(text=UpperCAmelCase     , add_special_tokens=UpperCAmelCase   ).input_ids
																							lowerCAmelCase   :Optional[int]						=			[self.tokenizer.cls_token_id] + input_ids
																							lowerCAmelCase   :Union[str, Any]						=			torch.tensor(UpperCAmelCase   ).unsqueeze(0   )
																							model_inputs.update({'input_ids': input_ids}   )

																	elif model_type == "pix2struct":
																							lowerCAmelCase   :Optional[Any]						=			self.image_processor(images=UpperCAmelCase     , header_text=UpperCAmelCase     , return_tensors=self.framework   )

																	elif model_type != "vision-encoder-decoder":
																							# vision-encoder-decoder does not support conditional generation
																							lowerCAmelCase   :int						=			self.image_processor(images=UpperCAmelCase     , return_tensors=self.framework   )
																							lowerCAmelCase   :Optional[Any]						=			self.tokenizer(UpperCAmelCase     , return_tensors=self.framework   )
																							model_inputs.update(UpperCAmelCase   )

																	else:
																							raise ValueError(f"""Model type {model_type} does not support conditional text generation"""   )

											else:
																	lowerCAmelCase   :str						=			self.image_processor(images=UpperCAmelCase     , return_tensors=self.framework   )

											if self.model.config.model_type == "git" and prompt is None:
																	lowerCAmelCase   :Union[str, Any]						=			None

											return model_inputs
					def        UpperCAmelCase__ (							self							:	Optional[Any]     , UpperCAmelCase							:	Union[str, Any]     , UpperCAmelCase							:	Any=None   )	->       int:
											# Git model sets `model_inputs["input_ids"] = None` in `preprocess` (when `prompt=None`). In batch model, the
											# pipeline will group them into a list of `None`, which fail `_forward`. Avoid this by checking it first.
											if (
											    "input_ids" in model_inputs
											    and isinstance(model_inputs['input_ids']     , UpperCAmelCase   )
											    and all(x is None for x in model_inputs['input_ids']   )
											):
																	lowerCAmelCase   :Optional[int]						=			None

											if generate_kwargs is None:
																	lowerCAmelCase   :Optional[int]						=			{}
											# FIXME: We need to pop here due to a difference in how `generation.py` and `generation.tf_utils.py`
											#  parse inputs. In the Tensorflow version, `generate` raises an error if we don't use `input_ids` whereas
											#  the PyTorch version matches it with `self.model.main_input_name` or `self.model.encoder.main_input_name`
											#  in the `_prepare_model_inputs` method.
											lowerCAmelCase   :Optional[int]						=			model_inputs.pop(self.model.main_input_name   )
											lowerCAmelCase   :Any						=			self.model.generate(UpperCAmelCase     , **UpperCAmelCase     , **UpperCAmelCase   )
											return model_outputs
					def        UpperCAmelCase__ (							self							:	Dict     , UpperCAmelCase							:	Union[str, Any]   )	->       Any:
											lowerCAmelCase   :str						=			[]
											for output_ids in model_outputs:
																	lowerCAmelCase   :Optional[int]						=			{
																	    'generated_text': self.tokenizer.decode(
																	        UpperCAmelCase     , skip_special_tokens=UpperCAmelCase     , )
																	}
																	records.append(UpperCAmelCase   )
											return records